Can integrating the Memory Support Intervention into cognitive therapy improve depression outcome? Study protocol for a randomized controlled trial

S T U D Y P R O T O C O L

Open Access

Can integrating the Memory Support

Intervention into cognitive therapy improve

depression outcome? Study protocol for a

randomized controlled trial

Allison G. Harvey

, Lu Dong

, Jason Y. Lee

, Nicole B. Gumport

, Steven D. Hollon

, Sophia Rabe-Hesketh

,

Kerrie Hein

, Kirsten Haman

, Mary E. McNamara

, Claire Weaver

, Armando Martinez

, Haruka Notsu

,

Garret Zieve

and Courtney C. Armstrong

Abstract

Background:

The Memory Support Intervention was developed in response to evidence showing that: (1) patient

memory for treatment is poor, (2) poor memory for treatment is associated with poorer adherence and poorer

outcome, (3) the impact of memory impairment can be minimized by the use of memory support strategies and

(4) improved memory for treatment improves outcome. The aim of this study protocol is to conduct a confirmatory

efficacy trial to test whether the Memory Support Intervention improves illness course and functional outcomes. As

a

“

platform

”

for the next step in investigating this approach, we focus on major depressive disorder (MDD) and cognitive

therapy (CT).

Method/design:

Adults with MDD (

n

= 178, including 20% for potential attrition) will be randomly allocated to CT +

Memory Support or CT-as-usual and will be assessed at baseline, post treatment and at 6 and 12 months

’

follow-up

(6FU and 12FU). We will compare the effects of CT + Memory Support vs. CT-as-usual to determine if the new

intervention improves the course of illness and reduces functional impairment (aim 1). We will determine if patient

memory for treatment mediates the relationship between treatment condition and outcome (aim 2). We will evaluate

if previously reported poor treatment response subgroups moderate target engagement (aim 3).

Discussion:

The Memory Support Intervention has been developed to be

“

transdiagnostic

”

(relevant to a broad range

of mental disorders) and

“

pantreatment

”

(relevant to a broad range of types of treatment). This study protocol

describes a

“

next step

”

in the treatment development process by testing the Memory Support Intervention for

major depressive disorder (MDD) and cognitive therapy (CT). If the results are promising, future directions will

test the applicability to other kinds of interventions and disorders and in other settings.

Trial registration:

ClinicalTrials.gov, ID: NCT01790919. Registered on 6 October 2016.

Keywords:

Memory support, Depression, Cognitive therapy, Transdiagnostic, Experimental therapeutics

* Correspondence:[email protected]

1_{Department of Psychology, University of California, Berkeley, 3210 Tolman}

Hall #1650, Berkeley, CA 94720-1650, USA

Full list of author information is available at the end of the article

Background

Patients accurately recall only about one third of the

recommendations made during a physician visit [1

–

8]

and

during

a

cognitive

behavior

therapy

(CBT)

session [9, 10]. In one study, 25% of patients

remem-bered recommendations that were

not

made [1].

Recall is particularly poor for advice for

health-behavior change [11]. Perhaps not surprisingly, poor

memory for the content of treatment is associated

with

poor

adherence

to

medical

treatments

for

chronic conditions [12

–

16], which is known to be

associated with worse outcome [17]. Also, poor

mem-ory for CBT is associated with poorer outcome [10].

We have offered various explanations for these findings

[18]. First, even when memory is functioning optimally,

fallibility is possible at initial encoding, storage or retrieval

[19]. Second, a CBT treatment session is typically 50 min

long, covers complex information, and can elicit negative

emotion. Negative emotion is associated with attentional

biasing and narrowing, which impacts encoding [20].

Third, the odds are stacked

against

people learning,

gener-alizing and transferring knowledge to new situations; this

is known as the transfer of learning problem [21, 22].

Fourth, memory deficits and biases are common across

mental disorders [23

–

27] and memory deficits are

associated with poorer memory for treatment [28, 29].

The good news is that the impact of memory

impair-ment can be minimized. Memory encoding and

reten-tion can be markedly improved by the use of memory

support techniques. This has been demonstrated in

medical visits [13, 30], and among older adults [31] as

well as for people with memory impairments associated

with Alzheimer

’

s disease, vascular dementia [32] and

frontal lobe dysfunction [33].

We developed and tested the Memory Support

Inter-vention, which is designed to improve patient memory

for treatment

. This approach is

not

intended to have a

direct effect on improving memory functioning per se.

The Memory Support Intervention was distilled from

the cognitive science and education literature based on

carefully honed criteria [18]. A small underpowered trial

has provided preliminary evidence that the intervention

exerts a measurable effect on patient memory for

treatment and demonstrates a clinical effect [34].

The Memory Support Intervention has been developed

to be

“

transdiagnostic

”

(relevant to a broad range of mental

disorders) and

“

pantreatment

”

(relevant to a broad range of

types of treatment). However, investigating these possible

broader applications seems premature without first

estab-lishing efficacy, which is the aim of the study described in

this paper. To create a platform for the

“

next step

”

in

inves-tigating the approach, we will recruit patients who meet

diagnostic criteria for major depressive disorder (MDD)

and focus on one biopsychosocial intervention

—

cognitive

therapy (CT). If the results are promising, future directions

will test the applicability to other kinds of interventions and

disorders and in other settings.

Why focus on MDD? First, MDD is prevalent and

causes impairment [35, 36]; second, there is a need to

improve treatments for MDD because a proportion of

patients do not recover and of those who do recover, the

majority relapse [37]; third, memory is poor in MDD

and is modifiable [38, 39]; fourth, preliminary data

sug-gest that improving memory for treatment in MDD

improves outcome [34, 40, 41].

Why focus on CT for MDD? CT for MDD is well

articu-lated and has been widely studied. Meta-analyses confirm

CT as a frontline treatment [42, 43]. Despite these

impres-sive outcomes, there is room for improvement [44, 45].

This study protocol addresses two additional

consider-ations. First, older age, lower intelligence and chronic

depression each predict poorer response to treatment

for depression, including CT [46]. Impairment in

de-clarative memory is also associated with poorer outcome

[47

–

50]. Moreover, fewer years of education has been

associated with a positive response to CT + Memory

Support relative to more years of education [34]. We

hypothesize that all of these poorer response groups

may derive special benefit from the Memory Support

Intervention. Hence, while we expect all patients to

benefit from the Memory Support Intervention, we

have planned analyses to determine if these subgroups

stand to gain the greatest advantage from

experimen-tal facilitation of memory for treatment. Second, there

is evidence that improved mood in MDD is associated

with improved declarative memory [51], although

re-mitted MDD patients still exhibit significant memory

deficits [23]. We have planned analyses to address

this potential confound.

Task. The third aim is to evaluate if previously reported

poor treatment response subgroups moderate target

en-gagement. We expect that the relationship between

mem-ory support dose and outcome will be positive and

stronger among those who are older, have lower IQ, have

more chronic depression, have poorer baseline declarative

memory performance and have fewer years of education.

Method/design

Study design and setting

This is a prospective randomized controlled study.

Adults (

n

= 178) who meet criteria for MDD will be

randomly assigned, in a 1:1 parallel group design, to CT

+ Memory Support or CT-as-usual (see Fig. 1 for a flow

chart of the study design). Randomization is stratified by

age (

≤

49, 50 + years) and depression chronicity (< 2 years,

≥

2 years) [46]. Participants receive a battery of outcome

measures pre-treatment and post-treatment (i.e., within

2 weeks after the final treatment session) and at 6 and

12 months

’

follow-up. Additional assessments of patient

and therapist memory for treatment take place in weeks

4, 8, 12 and 16 of treatment for the mediation analysis.

The assessment team is blind to treatment allocation.

Randomization will be conducted using a computerized,

random-number generator where the planned stratified

randomization is a part of the generation of the

alloca-tion sequence. Only the project coordinators in charge

of randomization and of the Memory Support Rating

Scale (MSRS) scoring know the treatment allocation of

each participant. A Data Safety and Monitoring Board

(DSMB) will review the study every 6 months during the

active treatment phase. The Standard Protocol Items:

Recommendations for Interventional Trials (SPIRIT

2013) Checklist (see Additional file 1) and Figure (see

Fig. 2 Standard Protocol Item: Recommendation for

Interventional Trials diagram) are provided [52].

For participants who discontinue, the assessment team

will endeavor to collect all assessment data, prioritizing

the primary outcomes.

Participants

A total of 178 adults who meet the criteria will be

re-cruited. Recruitment will be conducted in the Bay Area,

CA, United States by clinician referral and advertisements.

Eligibility is assessed first by a phone interview and then,

if the individual is eligible after the phone interview, a

more detailed, in-person interview. To enhance

represen-tativeness and generalizability, the inclusion and exclusion

criteria are kept to a minimum.

Inclusion criteria

The inclusion criteria are:

Age 18 + years

Willing and able to give consent. Participants must

consent to being video-recorded (necessary for

MSRS scoring) and to NIMH data sharing

English language fluency

Diagnosis of major depressive disorder (MDD), first

episode, recurrent or chronic according to the

Diagnostic and Statistical Manual of Mental

Disorders, Fifth Edition

(DSM-5)

Minimum score 26 or above on the Inventory of

Depressive Symptomatology, Self-Report (IDS-SR).

This cutoff denotes at least

“

moderate

”

depression

If taking medications for mood, medications must be

stable for the past 4 weeks

Exclusion criteria

The exclusion criteria are:

History of bipolar disorder

History of psychosis or psychotic features

Lifetime history of failure to respond to four or

more sessions of CBT/CT for depression

Current non-psychotic disorder if it constitutes the

principal diagnosis and if it requires treatment other

than that offered in the project.

“

Principal

”

is defined

as the disorder currently most distressing and

disabling, using a widely accepted severity rating

scale capturing distress and interference (0

–

8, 4 +

indicates clinical severity)

Moderate or severe substance use in the past

6 months where

“

moderate

”

is defined as 4

–

5

symptoms and

“

severe

”

is defined as 6 + symptoms

of those listed in DSM-5 for each of the

substance-related disorders

Evidence of any medical disorder or condition that

could cause depression, preclude participation in

CT, or is associated with memory problems, that is

not currently stabilized and/or managed under the

care of a physician or the presence of an active and

progressive physical illness or neurological

degenerative disease

Current suicide risk sufficient to preclude treatment

on an outpatient basis (assessed by the

Columbia-Suicide Severity Rating Scale) or current homicide

risk (assessed by our staff or referring treatment

provider)

Pregnancy or breastfeeding

Not able/willing to participate in and/or complete

the pre-treatment assessments

stable doses for 4 weeks prior to randomization and

(2) medication use and changes, along with

participa-tion in other treatments/therapy, will be recorded. All

medication decisions will ultimately rest with the

treating physician and participant.

Following the precedent set in prior research [54],

participants with a lifetime history of failure to

re-spond to four or more sessions of CBT for depression

will be excluded. Participants who have engaged in

moderate or severe substance use in the past 6 months

will also be excluded.

“

Moderate

”

substance use is

defined as four to five symptoms and

“

severe

”

substance use is defined as six or more of the

symptoms listed in the DSM-5 for each of the

substance-related disorders.

Measures

The primary and secondary outcomes are presented in

Additional file 2. Additional file 3 lists all of the

mea-sures as well as the timing for administration. Except

where specified in Additional file 3, the measures

de-scribed below will be delivered at each assessment point.

In addition to demographics (age, contact information,

gender, race/ethnicity, family, education, employment,

living arrangements, government assistance, housing)

and assessment of medical history the following

measures will be administered:

Diagnostic

To evaluate current and past psychiatric disorders the

will be administered along with the

Longitudinal

Interval Follow-up Evaluation (LIFE)

[57] to further

ascertain presence/absence of MDD, number and length

of mood episodes and remission, response, relapse,

recovery, recurrence and time to relapse or recurrence

(see Additional file 1 for definitions which are drawn

from the ACNP criteria [58]).

Symptomatic

The

IDS-SR

[59], a widely used self-report measure of

depression severity, as well as two subscales (ideation

and behavior) from the lifetime and current version of

the Columbia-Suicide Severity Rating Scale (C-SSRS)

[60

–

62], will be administered. The

Quick Inventory for

Depression Symptomatology

(QIDS) is administered

every treatment session [38] for clinical purposes

(moni-toring of suicide and depression symptoms).

Functional impairment

The

World Health Organization Disability Assessment

Schedule 2.0 (WHODAS 2.0)

[39] as well as the

four-question

“

Healthy Days

”

(CDC HRQOL-4) core module

developed by the CDC [63] will be administered.

IQ

The

National Adult Reading Test (NART)

[64] estimates

premorbid intelligence. The total number of errors are

calculated and used to derive an estimate of full-scale IQ

and verbal IQ.

Memory

The

Patient Treatment Recall Task

[10] and the

Generalization Task

[65] are included as early indicators

of target engagement and the trajectory of recall and

learning over time.

Classical tests of memory

include a test

of declarative memory

—

the

Episodic Face-Name Learning

Task

[66

–

68]

—

which is the domain in which the most

profound declines in MDD are observed [69

–

74].

Working memory will be assessed via the

N-Back

[75]

because working memory aids the formation and

reten-tion of long-term declarative memories via attentive and

elaborative-rehearsal processes [76, 77].

Memory Support Rating Scale (MSRS) [78]

Selected treatment session video-recordings will be

coded using the

MSRS

to establish the dose of memory

support delivered.

receiving and any changes in those treatments

through-out the duration of the study.

Treatment credibility/expectancy

is administered at

session 2, post-treatment, 6FU and 12FU via the

Credibility/Expectancy Questionnaire

[79, 80].

Exploratory measures

As this is a relatively new program of research and we

wish to learn as much as possible, we have included the

following measures on a pilot/exploratory basis:

Patient

Usefulness and Utilization of Cognitive Therapy Skills

Scale

(delivered post treatment, 6FU and 12FU), the

Competencies in Cognitive Therapy Scale

[81] (delivered at

post treatment, 6FU and 12FU),

Patient Conceptualization

of Depression Task

(delivered at the pre-treatment

assess-ment, post treatment and 6FU) and a

Memory Support

Treatment Provider Checklist

to be completed by the

ther-apists delivering CT + Memory Support (delivered during

weeks 4, 8, 12 and 16 of the treatment).

Treatments

Both treatments are comprised of 20

–

26 × 50-min

sessions conducted over 16 weeks.

CT-as-usual

CT was developed by Beck et al. [82] and has

incor-porated a number of innovations [83, 84]. Treatment

maneuvers are designed to identify, reality test, and

correct distorted beliefs and information processing

[82]. CT for MDD will be conducted according to the

standard manuals [82, 85, 86].

CT + Memory Support

The Memory Support Intervention is a manualized

ad-junctive treatment that will be delivered alongside

CT-as-usual. The Memory Support Intervention is designed

to improve patient memory for treatment. Distilled from

the cognitive science and education literature based on

carefully honed criteria [18], the Memory Support

Inter-vention is comprised of eight memory-promoting

strat-egies: attention recruitment, categorization, evaluation,

application, repetition, practice remembering, cue-based

reminder and praise recall. These strategies are

pro-actively, strategically and intensively integrated into

treatment-as-usual to support encoding. Memory

sup-port is delivered alongside each

“

treatment point,

”

defined as a main idea, principle, or experience that the

treatment provider wants the patient to remember or

implement as part of the treatment [10]. We

acknow-ledge that some memory support is a standard part of

certain treatments, including CBT [84, 87]. In the

present study, the two treatment arms will differ in the

“

dose

”

of memory support. An underpowered pilot study

indicated

that

the

Memory

Support

Intervention

effectively increases the amount of memory support

de-livered and improves depression outcome on certain

measures [34].

Intervention fidelity

Treatment session video-recordings that occur at session

2 and at weeks 4, 8, 12 and 16 are coded using the

MSRS [78] to establish the dose of memory support

de-livered. CT + Memory Support therapists are blind to

which sessions are coded. CT-as-usual therapists are

blind to the memory support portion of the research.

To reduce expectations impacting the delivery of each

arm and to ensure purity of delivery [88], each treatment

provider will be allocated to deliver only one of the two

treatments. The rationale is that therapists report that

once they have mastered memory support it is difficult

to deliver CT-as-usual.

Clinicians use a treatment manual and receive weekly

supervision to standardize treatment administration.

Treatment sessions are video- and audio-taped and a

random selection are rated using the

Cognitive Therapy

Rating Scale

[89] which is a measure of general

interven-tion skills and CBT-specific skills. The goal of this

inter-vention is to maintain consistency and ensure adherence

to the protocols [89].

Data analysis

Preliminary data evaluation

Missing or aberrant data will be verified. Data will be

audited for quality and completeness. Evaluation of

dis-tributions detects outliers and ensures that assumptions

of planned analyses are met. Baseline differences

be-tween groups will be examined (e.g., IQ, demographics,

psychiatric and medical comorbidity, medications).

Stat-istical tests will not be used to select covariates in the

primary intent-to-treat analysis [90

–

92]. Instead, the

potential influences of baseline differences will be

evalu-ated as moderators. If remedial training is given to a

therapist whose adherence to the treatment protocol is

not satisfactory, this will be included as a dummy-coded

covariate in all analyses.

Multiple testing

We will use alpha = .05 for each primary hypothesis. For

multiple testing conducted within each main hypothesis,

we will adopt a stepwise multiple comparison procedure

that is considered more powerful than the widely used

Bonferroni correction [93, 94].

Missing data

will be added as predictors to reduce bias due to data

missing not at random.

Hypothesis 1

CT + Memory Support will be superior to CT-as-usual

for improving course of illness and reducing functional

impairment at post-treatment, 6FU and 12FU on

primary outcomes

listed in Additional file 1. Treatment

groups will be compared on categorical outcomes (e.g.,

remission, relapse) using logistic regression to evaluate

the odds of remission at post-treatment, and the odds of

relapse at 6FU and 12FU. For the continuous variables

(e.g., IDS-SR), we will test for differences in the mean

trajectories across time between CT + Memory Support

and CT-as-usual using Hierarchical Linear Modeling

(HLM) [96

–

98]. The 1st level will represent

within-person variation, and will include time indicators (or

dummy variables) as predictors (post-treatment, 6FU

and 12FU, with baseline as reference). The 2nd level

rep-resents between-person variation in the intercept and

coefficients of the time indicators, and will include a

dummy variable for arm (CT + Memory Support vs.

CT-as-usual) as the predictor variable. Interactions between

arm and the time indicators will be retained only if

found to be significant at the 5% level using

likelihood-ratio tests. Significant interaction terms between arm

and time indicator suggest that there are different

trajec-tories across time and between arms, and will be

graphed to interpret the interaction.

Hypothesis 2

Relative to CT-as-usual, the relationship between CT

+ Memory Support and better treatment outcome will

be

mediated

by

patient

memory

for

treatment

.

Outcome will be measured by IDS-SR at post-treatment,

6FU and 12FU. Patient memory will be measured by the

Patient Treatment Recall Task and the Generalization

Task at weeks 4, 8, 12 and 16 of treatment. A mediation

model will be specified using Structural Equation

Modeling [99]. Indirect effects from CT + Memory

Support to better treatment outcome via patient

memory will be assessed.

Hypothesis 3

The relationship between treatment condition and

out-come will be moderated by poorer response subgroups;

namely, older age, lower IQ, more chronic depression,

poorer baseline declarative memory performance and

fewer years of education. IQ will be estimated with the

NART. Chronicity will be defined as current episode

greater than or equal to 2 years [46]. Declarative

mem-ory will be quantified as described in Additional file 1.

To assess whether Memory Support is more beneficial

for any of the patient subgroups, moderation will be

tested as an interaction between potential moderator

and treatment arm; in the HLMs, the interaction

be-tween any of these variables, time, and treatment group

will be tested.

Intervention fidelity check

MSRS scores will be compared for CT + Memory

Sup-port vs. CT-as-usual via independent samples

t

tests. To

test the assumption that memory for CT session content

improves as a result of CT + Memory Support we will

use HLMs to analyze differences in linear rates of

change between the two groups on the Patient

Treat-ment Recall Task and Generalization Task delivered

during weeks 4, 8, 12 and 16 of the treatment phase and

at 6FU and 12FU.

Additional planned analyses

1.

Is CT + Memory Support associated with longer

time-to-relapse relative to CT-as-usual?

Survival analyses

[

100

] will be conducted. The Kaplan-Meier product

limit method will be used to generate survival curves

for the two treatment conditions [

101

]. Cox

regression will be applied to test for group

differences.

2.

Are demographics, psychiatric and medical

comorbidity, medications and symptom severity

moderators?

This will be tested by adding the

product of arm and the moderator variable to

regression models or HLMs [

102

,

103

]. A significant

coefficient for the interaction term would indicate a

moderating effect, and will be followed up with

graphs to determine the nature of the effect

modification [

102

].

3.

Does memory for treatment improve simply because

memory deficits resolve with successful treatment?

Linear regressions will evaluate whether change in

pre-post IDS and pre-post declarative and working

memory each independently predict week 4 to

post-treatment differences in Patient Recall Task

per-formance. If significant, we will conduct a

hierarchical linear regression to evaluate whether

pre-post change in IDS scores predicts pre-post

change in Patient Treatment Recall Task

performance beyond pre-post improvement in

memory.

5.

Number of sessions received

(range 20

–

26 sessions)

will be added as a covariate to control for differences

in this across participants.

Power analysis

Based on the pilot study [34], using Glimmpse [104,

105] for repeated measures design, we took into account

the four repeated measurements (pre-treatment,

post-treatment, 6FU, 12FU). At an alpha level of 0.05 with

80% power, the sample size is estimated to be 80 for the

IDS-SR. We also used G*Power and the Demidenko

[106] method to calculate estimated sample size for

bin-ary mood outcomes (i.e., remission, relapse). The sample

sizes are estimated to be 103 (remission) and 118

(re-lapse). Finally, Fritz and MacKinnon [107] provide

rec-ommendations for mediation sample size, and estimates

from pilot data indicate that the recommended sample

size is 148. Adding an additional 20% for possible

attri-tion, we propose a sample of 178.

Discussion

This study protocol addresses several research priorities.

First, following the experimental therapeutics approach

[108], this study protocol describes a confirmatory

effi-cacy trial to provide a definitive test of the hypothesis

that a novel target

—

patient memory for the contents of

treatment

—

probed with a novel intervention

—

the

Mem-ory Support Intervention

—

will improve clinical

out-come. Second, a novel intervention derived from basic,

non-patient research in cognitive science and education,

will be tested in the service of improving outcomes for

patients with severe mental disorders. Third, the

Mem-ory Support Intervention has been derived to be

“

transdiagnostic

”

(relevant to a broad range of mental

disorders) and

“

pantreatment

”

(relevant to a broad range

of types of treatment). However, investigating these

pos-sible broader applications seems premature without first

establishing efficacy, which is the aim of the study

de-scribed in this protocol. If the results are promising,

fu-ture directions will test the applicability to a broad range

of biopsychosocial interventions for a wide range of

pa-tient populations being treated at various medical and

mental health settings.

Trial status

The trial is funded for 4 years (Project ID Number:

R01MH108657). The research staff team started setting

up the study in September, 2016. Patients were first

ran-domized in December, 2016. The treatment phase will

be completed by August of 2019. Final outcome

assess-ments will be complete by August of 2020.

Endnotes

This was added in July, 2017. NIH/NIMH

data-sharing

requirements

necessitated

reconsent

of

participants already randomized. If a patient does not

agree to data sharing we have been instructed to exclude

them from the analysis.

Additional files

Additional file 1:SPIRIT 2013 Checklist. (DOC 122 kb)

Additional file 2:Summary of primary and secondary outcome(s). (DOCX 13 kb)

Additional file 3:Timeframe for Assessments. Table indicating time points where assessments are conducted. (DOCX 46 kb)

Abbreviations

12FU:12-months’follow-up assessment; 6FU: 6-months’follow-up assess-ment; ACNP: American College of Neuropsychopharmacology;

CBT: Cognitive behavior therapy; CEQ: Credibility/Expectancy Questionnaire; C-SSRS: Columbia-Suicide Severity Rating Scale; CT: Cognitive therapy; CTRS: Cognitive Therapy Rating Scale; DSM-5:Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; DSMB: Data and Safety Monitoring Board; HLM: Hierarchical Linear Modeling; IDS-SR: Inventory of Depressive Symptoms, Self-Report; LIFE: Longitudinal Interval Follow-up Evaluation; MDD: Major depressive disorder; MSRS: Memory Support Rating Scale; NART: National Adult Reading Test; NIMH: National Institutes of Mental Health; QIDS: Quick Inventory of Depressive Symptoms; SEM: Structural Equation Modeling; SPIRIT 2013: Standard Protocol Items: Recommendations for Interventional Trials; WHODAS 2.0: The World Health Organization Disability Assessment Schedule 2.0

Acknowledgements

This study is funded by the National Institute of Mental Health (R01MH108657). The DSMB is composed of three members: Dr. John McQuaid, PhD, Dr. Jutta Joormann, PhD and Dr. Hao Wu.

The DSMB reviews progress and safety of study procedures twice per year and are responsible for safe guarding the interests of study participants. This committee is independent of the sponsor.

Funding

This study is funded by the National Institute of Mental Health (R01MH108657). The funding agency has/had no role in the design, collection, management, analysis, or interpretation of data; the writing of the manuscript; or the decision to submit the study protocol for publication. The funding agency has no ultimate authority over any of these activities.

Availability of data and materials

Not applicable

Authors_’contributions

AGH developed the study concept. AGH, LD, JL, SH and S R-H contributed to the acquisition of the funding. AGH and JL contributed to developing the Memory Support Intervention. SDH, KHa and AGH trained the therapists. AGH, NBG, KHe, MM, CW, HN, AM, GZ and CA are responsible for the acquisition of data and the measurement of memory. LD, AM and S R-H are responsible for the analysis and interpretation of data. AGH drafted the manuscript. All authors have been involved in revising the manuscript. All authors read and approved the final manuscript. Other than the authors and compliance with data-sharing agreements stipulated by NIH, no other entities have contractual agreements with regard to access to the final dataset.

Ethics approval and consent to participate

followed by a written informed consent which is obtained at the beginning of the pre-assessment, which confirms eligibility, by a member of the assessment team. Adverse events and other unintended effects will be report to CPHS and NIH, following the rules stipulated by these two oversight bodies. If important protocol modifications are made these will be reviewed by CPHS and reported on ClinicalTrials.gov.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Publisher

’

s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author details

1_{Department of Psychology, University of California, Berkeley, 3210 Tolman}

Hall #1650, Berkeley, CA 94720-1650, USA.2Vanderbilt University, Nashville, TN, USA.

Received: 25 August 2017 Accepted: 23 October 2017

References

1. Bober SL, Hoke LA, Duda RB, Tung NM. Recommendation recall and satisfaction after attending breast/ovarian cancer risk counseling. J Genet Couns. 2007;16:755–62.

2. Jansen J, Butow PN, van Weert JC, van Dulmen S, Devine RJ, Heeren TJ, et al. Does age really matter? Recall of information presented to newly referred patients with cancer. J Clin Oncol. 2008;26:5450–7.

3. Pickney CS, Arnason JA. Correlation between patient recall of bone densitometry results and subsequent treatment adherence. Osteoporos Int. 2005;16:1156–60.

4. Lewkovich GN, Haneline MT. Patient recall of the mechanics of cervical spine manipulation. J Manipulative Physiol Ther. 2005;28:708–12. 5. Croyle RT, Loftus EF, Barger SD, Sun YC, Hart M, Gettig J. How well do

people recall risk factor test results? Accuracy and bias among cholesterol screening participants. Health Psychol. 2006;25:425–32.

6. Mcguire LC. Remembering what the doctor said: organization and adults’ memory for medical information. Exp Aging Res. 1996;22(4):403_–28. 7. Ley P. Memory for medical information. Br J Soc Clin Psychol. 1979;18:

245–55.

8. Jansen J, van Weert J, van der Meulen N, van Dulmen S, Heeren T, Bensing J. Recall in older cancer patients: measuring memory for medical information. Gerontologist. 2008;48(2):149–57.

9. Chambers MJ. Patient recall of recommendations in the behavioural treatment of insomnia. Sleep Res. 1991;20:222.

10. Lee J, Harvey AG. Memory for therapy in bipolar disorder and comorbid insomnia. J Consult Clin Psychol. 2015;83:92–102. PubMed Central PMCID: PMCPMC4323885.

11. Flocke SA, Stange KC. Direct observation and patient recall of health behavior advice. Prev Med. 2004;38:343_–9.

12. Tosteson AN, Grove MR, Hammond CS, Moncur MM, Ray GT, Hebert GM, et al. Early discontinuation of treatment for osteoporosis. Am J Med. 2003; 115:209–16.

13. Cox DJ, Tisdelle DA, Culbert JP. Increasing adherence to behavioral homework assignments. J Behav Med. 1988;11(5):519–22.

14. Linn AJ, van Dijk L, Smit EG, Jansen J, van Weert JC. May you never forget what is worth remembering: the relation between recall of medical information and medication adherence in patients with inflammatory bowel disease. J Crohn’s Colitis. 2013;7(11):e543–50.

15. Ley P, Jain V, Skilbeck C. A method for decreasing patients’medication errors. Psychol Med. 1977;6(04):599–601.

16. Kravitz RL, Hays RD, Sherbourne CD, DiMatteo MR, Rogers WH, Ordway L, et al. Recall of recommendations and adherence to advice among patients with chronic medical conditions. Arch Intern Med. 1993;153:1869–78. 17. Simpson HB, Maher MJ, Wang Y, Bao Y, Foa EB, Franklin M. Patient

adherence predicts outcome from cognitive behavioral therapy in obsessive-compulsive disorder. J Consult Clin Psychol. 2011;79(2):247.

18. Harvey AG, Lee J, Williams J, Hollon SD, Walker MP, Thompson MA, et al. Improving outcome of psychosocial treatments by enhancing memory and learning. Perspect Psychol Sci. 2014;9:161–79. PubMed Central PMCID: PMCPMC4276345.

19. Schacter D. The seven sins of memory: how the mind forgets and remembers. New York City: Houghton Mifflin; 2001.

20. Easterbrook JA. The effect of emotion on cue utilization and the organization of behavior. Psychol Rev. 1959;66:183_–201. PubMed PMID: 1961-03074-001. 21. Thorndike EL. The fundamentals of learning. New York: Teacher’s College

Bureau of Publications; 1932.

22. Barnett SM, Ceci SJ. When and where do we apply what we learn? A taxonomy for far transfer. Psychol Bull. 2002;128:612.

23. Behnken A, Schöning S, Gerss J, Konrad C, de Jong-Meyer R, Zwanzger P, et al. Persistent non-verbal memory impairment in remitted major

depression—caused by encoding deficits? J Affect Disord. 2010;122:144–8. 24. Robinson LJ, Thompson JM, Gallagher P, Goswami U, Young AH, Ferrier IN, et al. A meta-analysis of cognitive deficits in euthymic patients with bipolar disorder. J Affect Disord. 2006;93:105–15.

25. Varga M, Magnusson A, Flekkoy K, David AS, Opjordsmoen S. Clinical and neuropsychological correlates of insight in schizophrenia and bipolar I disorder: does diagnosis matter? Compr Psychiatry. 2007;48:583–91. 26. Jelinek L, Jacobsen D, Kellner M, Larbig F, Biesold K-H, Barre K, et al. Verbal

and nonverbal memory functioning in posttraumatic stress disorder (PTSD). J Clin Exp Neuropsychol. 2006;28:940_–8.

27. Airaksinen E, Larsson M, Forsell Y. Neuropsychological functions in anxiety disorders in population-based samples: evidence of episodic memory dysfunction. J Psychiatr Res. 2005;39:207_–14.

28. Bruce JM, Hancock LM, Arnett P, Lynch S. Treatment adherence in multiple sclerosis: association with emotional status, personality, and cognition. J Behav Med. 2010;33(3):219_–27.

29. Insel K, Morrow D, Brewer B, Figueredo A. Executive function, working memory, and medication adherence among older adults. J Gerontol B Psychol Sci Soc Sci. 2006;61(2):102–7.

30. Ley P, Bradshaw P, Eaves D, Walker C. A method for increasing patients’ recall of information presented by doctors. Psychol Med. 1973;3:217_–20. 31. Bamidis PD, Vivas AB, Styliadis C, Frantzidis C, Klados M, Schlee W, Siountas

A, Papageorgiou SG. A review of physical and cognitive interventions in aging. Neurosci Biobehav Rev. 2014;44:206_–20.

32. Almkvist O, Fratiglioni L, Agüero-Torres H, Viitanen M, Bäckman L. Cognitive support at episodic encoding and retrieval: similar patterns of utilization in community-based samples of Alzheimer_’s disease and vascular dementia patients. J Clin Exp Neuropsychol. 2010;21:816–30.

33. Bunce D. Cognitive support at encoding attenuates age differences in recollective experience among adults of lower frontal lobe function. Neuropsychology. 2003;17:353–61.

34. Harvey AG, Lee J, Smith RL, Gumport NB, Hollon SD, Rabe-Hesketh S, et al. Improving outcome for mental disorders by enhancing memory for treatment. Behav Res Ther. 2016;81:35_–46.

35. Lopez AD, Mathers CD. Measuring the global burden of disease and epidemiological transitions: 2002–2030. Ann Trop Med Parasitol. 2006; 100(5-6):481_–99.

36. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3(11):e442.

37. Eaton WW, Shao H, Nestadt G, Lee BH, Bienvenu OJ, Zandi P. Population-based study of first onset and chronicity in major depressive disorder. Arch Gen Psychiatry. 2008;65(5):513–20.

38. Rush A, Trivedi M, Ibrahim H, Carmody T, Arnow B, Klein D, et al. The 16-item Quick Inventory of Depressive Symptomatology (QIDS) Clinician Rating (QIDS-C) and Self-Report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003;54:573–83. 39. World Health Organization. 2.0: WHODAS 2.0. 2011.

40. Dong L, Lee JY, Harvey AG. Memory support strategies and bundles: a pathway to improving cognitive therapy for depression? J Consult Clin Psychol. 2017;85(3):187.

41. Dong L, Zhao X, Ong SL, Harvey AG. Patient recall of specific cognitive therapy contents predicts adherence and outcome. Behav Res Ther. 2017; 97:189–99.

43. Cuijpers P, Berking M, Andersson G, Quigley L, Kleiboer A, Dobson KS. A meta-analysis of cognitive-behavioural therapy for adult depression, alone and in comparison with other treatments. Can J Psychiatry. 2013;58:7. 44. Bockting CL, Hollon SD, Jarrett RB, Kuyken W, Dobson K. A lifetime

approach to major depressive disorder: the contributions of psychological interventions in preventing relapse and recurrence. Clin Psychol Rev. 2015; 41:16_–26.

45. Jarrett RB, Minhajuddin A, Kangas JL, Friedman ES, Callan JA, Thase ME. Acute phase cognitive therapy for recurrent major depressive disorder: who drops out and how much do patient skills influence response? Behav Res Ther. 2013;51(4):221–30.

46. Fournier JC, DeRubeis RJ, Shelton RC, Hollon SD, Amsterdam JD, Gallop R. Prediction of response to medication and cognitive therapy in the treatment of moderate to severe depression. J Consult Clin Psychol. 2009;77(4):775.

47. Majer M, Ising M, Künzel H, Binder EB, Holsboer F, Modell S, et al. Impaired divided attention predicts delayed response and risk to relapse in subjects with depressive disorders. Psychol Med. 2004;34:1453_–63.

48. Bearden CE, Glahn DC, Monkul ES, Barrett J, Najt P, Villarreal V, et al. Patterns of memory impairment in bipolar disorder and unipolar major depression. Psychiatry Res. 2006;142(2):139_–50.

49. Deuschle M, Kniest A, Niemann H, Erb-Bies N, Colla M, Hamann B, et al. Impaired declarative memory in depressed patients is slow to recover: clinical experience. Pharmacopsychiatry. 2004;37(04):147_–51. 50. Bremner JD, Vythilingam M, Vermetten E, Vaccarino V, Charney DS.

Deficits in hippocampal and anterior cingulate functioning during verbal declarative memory encoding in midlife major depression. Am J Psychiatry. 2004;161(4):637–45.

51. Douglas KM, Porter RJ. Longitudinal assessment of neuropsychological function in major depression. Aust N Z J Psychiatry. 2009;43(12):1105–17. 52. Chan A-W, Tetzlaff JM, Gøtzsche PC, Altman DG, Mann H, Berlin JA, et al. SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials. BMJ. 2013;346:e7586.

53. Daumit GL, Dickerson FB, Wang N-Y, Dalcin A, Jerome GJ, Anderson CA, et al. A behavioral weight-loss intervention in persons with serious mental illness. N Engl J Med. 2013;368(17):1594–602.

54. McGrath CL, Kelley ME, Dunlop BW, Holtzheimer III PE, Craighead WE, Mayberg HS. Pretreatment brain states identify likely nonresponse to standard treatments for depression. Biol Psychiatry. 2014;76(7):527–35. 55. First MB, Spitzer RL, Gibbon M, Williams JBW. Structured clinical interview

for DSM-V Axis 1 disorders administration booklet. New York: American Psychiatric Press; 2013.

56. First MB, Williams JBW, Karg RS, Spitzer RL. Structured Clinical Interview for DSM-5 Disorders (SCID-5-RV, FEB 2014 revision). New York: Biometrics Research Department, Columbia University; 2014.

57. Keller MB, Lavori PW, Friedman B, Nielsen E, Endicott J, McDonald S, et al. The longitudinal interval follow-up evaluation: a comprehensive method for assessing outcome in prospective longitudinal studies. Arch Gen Psychiatry. 1987;44:540_–8. PubMed PMID: 1988-01549-001.

58. Rush AJ, Kraemer HC, Sackeim HA, Fava M, Trivedi MH, Frank E, et al. Report by the ACNP Task Force on response and remission in major depressive disorder. Neuropsychopharmacology. 2006;31(9):1841_–53.

59. Trivedi MH, Rush AJ, Ibrahim HM, Carmody TJ, Biggs MM, Suppes T, et al. The Inventory of Depressive Symptomatology, Clinician Rating (IDS-C) and Self-Report (IDS-SR), and the Quick Inventory of Depressive

Symptomatology, Clinician Rating (QIDS-C) and Self-Report (QIDS-SR) in public sector patients with mood disorders: a psychometric evaluation. Psychol Med. 2004;34:73–82.

60. Center for Drug Evaluation and Research. Guidance for industry: suicidal ideation and behavior: prospective assessment of occurrence in clinical trials. Rockville: U.S. Department of Health and Human Services, U.S. Food and Drug Administration; 2012.

61. Mundt JC, Greist JH, Jefferson JW, Federico M, Mann JJ, Posner K. Prediction of suicidal behavior in clinical research by lifetime suicidal ideation and behavior ascertained by the electronic Columbia-Suicide Severity Rating Scale. J Clin Psychiatry. 2013;74(9):887–93.

62. Posner K, Brown GK, Stanley B, Brent DA, Yershova KV, Oquendo MA, Currier GW, Melvin GA, Greenhill L, Shen S, Mann JJ. The Columbia–Suicide Severity Rating Scale: initial validity and internal consistency findings from three multisite studies with adolescents and adults. Am J Psychiatry. 2011;168(12): 1266–77.

63. Moriarty D, Zack M, Kobau R. The Centers for Disease Control and Prevention’s Healthy Days Measures: population tracking of perceived physical and mental health over time. Health Qual Life Outcomes. 2003;1(1):37.

64. Nelson HE, Willison J. National Adult Reading Test (NART). Windsor: NFER-Nelson; 1991.

65. Gumport NB, Williams JJ, Harvey AG. Learning cognitive behavior therapy. J Behav Ther Exp Psychiatry. 2015;48:164–9. PubMed Central PMCID: PMCPMC4426215.

66. Mander BA, Santhanam S, Saletin JM, Walker MP. Wake deterioration and sleep restoration of human learning. Curr Biol. 2011;21:R183_–4.

67. Miller SL, Celone K, DePeau K, Diamond E, Dickerson BC, Rentz D, et al. Age-related memory impairment associated with loss of parietal deactivation but preserved hippocampal activation. Proc Natl Acad Sci. 2008;105(6):2181–6.

68. Sperling R, Bates J, Chua E, Cocchiarella A, Rentz D, Rosen B, et al. fMRI studies of associative encoding in young and elderly controls and mild Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2003;74(1):44–50. 69. Taconnat L, Baudouin A, Fay S, Raz N, Bouazzaoui B, El-Hage W, et al.

Episodic memory and organizational strategy in free recall in unipolar depression: the role of cognitive support and executive functions. J Clin Exp Neuropsychol. 2010;32:719–27.

70. Danion JM, Willard-Schroeder D, Zimmermann MA, Grangé D, Schlienger JL, Singer L. Explicit memory and repetition priming in depression. Preliminary findings Arch Gen Psychiatry. 1991;48:707–11.

71. Jermann F, Van der Linden M, Adam S, Ceschi G, Perroud A. Controlled and automatic uses of memory in depressed patients: effect of retention interval lengths. Behav Res Ther. 2005;43:681–90.

72. Calev A, Erwin PG. Recall and recognition in depressives: use of matched tasks. Br J Clin Psychol. 1985;24:127–8.

73. Ellwart T, Rinck M, Becker ES. Selective memory and memory deficits in depressed inpatients. Depress Anxiety. 2003;17:197_–206.

74. Hertel PT, Milan S. Depressive deficits in recognition: dissociation of recollection and familiarity. J Abnorm Psychol. 1994;103:736–42. 75. Kirchner WK. Age differences in short-term retention of rapidly changing

information. J Exp Psychol. 1956;55:352–8.

76. Cowan N. What are the differences between long-term, short-term, and working memory? Prog Brain Res. 2008;169:323_–38.

77. Ericsson KA, Kintsch W. Long-term working memory. Psychol Rev. 1995;102(2):211.

78. Lee JY, Worrell FC, Harvey AG. The Development and Validation of the Memory Support Rating Scale (MSRS). Psychol Assess. 2016;28(6):715–25. 79. Devilly GJ, Borkovec TD. Psychometric properties of the credibility/

expectancy questionnaire. J Behav Ther Exp Psychiatry. 2000;31:73_–86. PubMed PMID: 2000-12660-001.

80. Devilly GJ, Spence SH. The relative efficacy and treatment distress of EMDR and a cognitive-behavior trauma treatment protocol in the amelioration of posttraumatic stress disorder. J Anxiety Disord. 1999;13:131–57. PubMed PMID: 1999-13238-007.

81. Strunk DR, Hollars SN, Adler AD, Goldstein LA, Braun JD. Assessing patients’ cognitive therapy skills: initial evaluation of the competencies of Cognitive Therapy Scale. Cogn Ther Res. 2014;38(5):559.

82. Beck AT. Cognitive therapy of depression. New York: Guilford Press; 1979. p. 425.

83. Bennett-Levy J, Butler G, Fennell MJV, Hackmann A, Mueller M, Westbrook D. The Oxford handbook of behavioural experiments. Oxford: Oxford University Press; 2004.

84. Beck JS. Cognitive therapy: basics and beyond. New York: Guilford Press; 1995.

85. Beck JS. Cognitive therapy: basics and beyond. 2nd ed. New York: Guilford Press; 2011.

86. Greenberger D, Padesky CA. Mind over mood: change how you feel by changing the way you think. New York: Guilford Press; 2015.

87. Beck JS. Cognitive therapy for challenging problems: what to do when the basics don_’t work. New York: Guilford Press; 2005. p. 324.

88. Manber R, Edinger JD, Gress JL, San Pedro-Salcedo MG, Kuo TF, Kalista T. Cognitive behavioral therapy for insomnia enhances depression outcome in patients with comorbid major depressive disorder and insomnia. Sleep. 2008;31:489–95.

89. Young J, Beck AT. The development of the Cognitive Therapy Scale. 1980. 90. Beach ML, Meier P. Choosing covariates in the analysis of clinical trials.

91. Canner PL. Covariate adjustment of treatment effects in clinical trials. Control Clin Trials. 1991;12:359_–66.

92. Senn SJ. Covariate imbalance and random allocation in clinical trials. Stat Med. 1989;8:467–75.

93. Nakagawa S. A farewell to Bonferroni: the problems of low statistical power and publication bias. Behav Ecol. 2004;15:1044_–5. PubMed PMID: 2004-19626-022.

94. Shaffer JP. Multiple hypothesis testing. Annu Rev Psychol. 1995;46(1):561–84. 95. Little RJ, Rubin DB. Statistical analysis with missing data. 2nd ed. New York:

Wiley; 2002.

96. Raudenbush S, Bryk A. Hierarchical linear models. Thousand Oaks: Sage; 2002.

97. Goldstein H. Multilevel statistical models. 4th ed. Chichester: Wiley; 2010. 98. Hox J. Multilevel analysis: techniques and applications. NY: Routledge; 2010. 99. Hayes AF. Introduction to mediation, moderation, and conditional process

analysis: a regression-based approach. New York: Guilford Press; 2013. 100. Greenhouse JB, Stangl D, Bromberg J. An introduction to survival analysis:

statistical methods for analysis of clinical trial data. J Consult Clin Psychol. 1989;57:536–44.

101. Kaplan E, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958;53:457–81.

102. Cohen J, Cohen P, West S, Aiken L. Applied multiple regression/correlation analysis for the behavioral sciences. 3rd ed. New Jersey: Lawrence Erlbaum Associates; 2003.

103. Jaccard J, Wan CK, Turrisi R. The detection and interpretation of interaction effects between continuous variables in multiple regression. Multivar Behav Res. 1990;25(4):467–78.

104. Guo Y, Logan HL, Glueck DH, Muller KE. Selecting a sample size for studies with repeated measures. BMC Med Res Methodol. 2013;13(1):100. 105. Kreidler SM, Muller KE, Grunwald GK, Ringham BM, Coker-Dukowitz ZT,

Sakhadeo UR, et al. GLIMMPSE: online power computation for linear models with and without a baseline covariate. J Stat Softw. 2013;54:10.

106. Demidenko E. Sample size determination for logistic regression revisited. Stat Med. 2007;26(18):3385_–97.

107. Fritz MS, MacKinnon DP. Required sample size to detect the mediated effect. Psychol Sci. 2007;18(3):233–9.

108. Insel TR, Gogtay N. National Institute of Mental Health clinical trials: new opportunities, new expectations. JAMA Psychiat. 2014;71(7):745_–6.

• We accept pre-submission inquiries

• Our selector tool helps you to find the most relevant journal • We provide round the clock customer support

• Convenient online submission • Thorough peer review

• Inclusion in PubMed and all major indexing services • Maximum visibility for your research

Submit your manuscript at www.biomedcentral.com/submit